eo-learn
ml4eo-bootcamp-2021
| eo-learn | ml4eo-bootcamp-2021 | |
|---|---|---|
| 7 | 1 | |
| 1,078 | 87 | |
| 0.5% | - | |
| 8.8 | 0.0 | |
| 3 months ago | over 2 years ago | |
| Python | Jupyter Notebook | |
| MIT License | - |
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eo-learn
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What are examples of well-organized data science project that I can see on Github?
I like ours https://github.com/sentinel-hub/eo-learn but of course I am biased
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Machine Learning free courses online for earth science - suggestions?
Sentinel Hub's eo-learn
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World Mosaic Time-lapse 2020 [3020 × 1510] [OC]
Overall description of used tools: - python: eo-learn and sentinelhub-py - gdal for GIS related stuff - ray for cluster computing
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Transitioning from NLP to satellite and image based CV
If you are joining a not small company they probably already have this, but an example is https://github.com/sentinel-hub/eo-learn which is specific to a certain set of satellite data products.
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Land Cover Classification of Istanbul, Turkey [4965x 2512]
Yes, `eo-learn` is just a collection of the existing tools you mention, which harmonizes the workflow for a specific task. Feel free to open ticket on eo-learn github if you have any questions! :)
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[OC] Vegetation of Africa 2019
* Python packages [sentinelhub-py](https://github.com/sentinel-hub/sentinelhub-py) and [eo-learn](https://github.com/sentinel-hub/eo-learn)
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[OC] Earth in a year (2019 True-Color Sentinel-2 L2A data)
Tools: - Python packages sentinelhub-py and eo-learn - GDAL 3.2
ml4eo-bootcamp-2021
What are some alternatives?
gdal - GDAL is an open source MIT licensed translator library for raster and vector geospatial data formats.
awesome-satellite-imagery-datasets - 🛰️ List of satellite image training datasets with annotations for computer vision and deep learning
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
techniques - Techniques for deep learning with satellite & aerial imagery
cpython - Alternative StdLib for Nim for Python targets, hijacks Python StdLib for Nim
ml-earth-observation-101 - An introduction to applying machine learning to satellite imagery (remote sensing).
pywsitest - PYthon WebSocket Integration TESTing framework
lidar-harmonization - Code release for Intensity Harmonization for Airborne LiDAR
eemeter - An open source python package for implementing and developing standard methods for calculating normalized metered energy consumption and avoided energy use.
fastai - The fastai deep learning library
styrofoam - (Alpha) Advanced WSGI router for running multiple separate WSGI applications
Awesome_Satellite_Benchmark_Datasets - Supplementary material for our paper "THERE IS NO DATA LIKE MORE DATA" is provided.